This proposal combines our metabolomics and radiation-signaling expertise with the expertise of team members in instrumentation for rapid and cost-effective assessment of select metabolites. The overall goals are to develop a reliable database of radiation metabolomic biomarkers in humans from easily-accessible biofluids, and then to refine subset(s) that will allow assessment of select biomarkers with instrumentation that could provide the basis for application in clinical and potentially in-field scenarios. The propose study builds on an extensive track record of accomplishments in radiation metabolomics, as well as the use of approaches, particularly differential mobility spectrometry (DMS), that allows for high-throughput assessment of metabolite biomarkers of interest. In the case of radiation metabolomics, our laboratory and its collaborators have made major contributions in establishing this field using a modern liquid chromatography (LC) mass spectrometry (MS) approach in a variety of animal models as well as in human cells. We have shown in publications over the last several years that they are dose-dependent and timecourse-dependent responses in urine metabolomics profiles after doses of ionizing radiation (IR) that are a NIAID priority. Recently, we have shown that there is evidence for specificity in the IR response in vivo compared to another relevant stressor, which mimics the inflammatory response to sepsis. Our team has also published extensively on the development and refinement of DMS, which should allow for selective tuning for metabolite biomarkers without cumbersome LC. We have demonstrated that DMS allows isolation of select metabolites so that they can then be detected with a simplified and miniature MS system. In the case of human biomarker development, we have a collection of biofluids from a large number of patients undergoing total body irradiation (TBI) and have already demonstrated significant metabolomic responses in urine after TBI. Aim 1 will be to develop a robust metabolomic biomarker database for human exposure using our high-end laboratory LCMS approach. Since dose and timecourse sampling is limited in TBI patients, we will use our extensive on-going mouse model datasets, which are funded by a different mechanism, as well as non-human primate samples provided by collaborators to model a much wider range of exposures. In the case of radiation toxicity, we have exciting preliminary data demonstrating that toxicity and lethality, which occurs approximately 2 wk after irradiation, can be distinguished as early as 1 day after irradiation in mice. Aim 2 will develop biomarker panels to distinguish IR biomarker signatures from other injury and disease processes. A modern bioinformatics pipeline, which includes novel in-house algorithms, has been developed to facilitate biomarker discovery. Having developed a robust IR dataset, we will then focus in aim 3 on development of convenient and cost-effective instrumentation that can provide the basis for use in clinical and ultimately in-field scenarios, and refine subsets of IR biomarkers that can be effectively measured with our approaches such as DMS-MS.